TY - GEN
T1 - Predicting high impact academic papers using citation network features
AU - McNamara, Daniel
AU - Wong, Paul
AU - Christen, Peter
AU - Ng, Kee Siong
PY - 2013
Y1 - 2013
N2 - Predicting future high impact academic papers is of benefit to a range of stakeholders, including governments, universities, academics, and investors. Being able to predict 'the next big thing' allows the allocation of resources to fields where these rapid developments are occurring. This paper develops a new method for predicting a paper's future impact using features of the paper's neighbourhood in the citation network, including measures of interdisciplinarity. Predictors of high impact papers include high early citation counts of the paper, high citation counts by the paper, citations of and by highly cited papers, and interdisciplinary citations of the paper and of papers that cite it. The Scopus database, consisting of over 24 million publication records from 1996-2010 across a wide range of disciplines, is used to motivate and evaluate the methods presented.
AB - Predicting future high impact academic papers is of benefit to a range of stakeholders, including governments, universities, academics, and investors. Being able to predict 'the next big thing' allows the allocation of resources to fields where these rapid developments are occurring. This paper develops a new method for predicting a paper's future impact using features of the paper's neighbourhood in the citation network, including measures of interdisciplinarity. Predictors of high impact papers include high early citation counts of the paper, high citation counts by the paper, citations of and by highly cited papers, and interdisciplinary citations of the paper and of papers that cite it. The Scopus database, consisting of over 24 million publication records from 1996-2010 across a wide range of disciplines, is used to motivate and evaluate the methods presented.
UR - http://www.scopus.com/inward/record.url?scp=84892894945&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-40319-4_2
DO - 10.1007/978-3-642-40319-4_2
M3 - Conference contribution
SN - 9783642403187
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 14
EP - 25
BT - Trends and Applications in Knowledge Discovery and Data Mining - PAKDD 2013 International Workshops
T2 - 17th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2013
Y2 - 14 April 2013 through 17 April 2013
ER -